How AI Moved the Advantage from Product to Proof

In a 2023 experiment run by GitHub, Microsoft and MIT, 95 professional programmers were given a standardised coding task, and those working with an AI assistant finished 55.8 per cent faster than the control group. That same year Erik Brynjolfsson and colleagues, writing for the National Bureau of Economic Research, tracked 5,179 customer support agents and found that access to a generative assistant raised issues resolved per hour by 14 per cent, with much larger gains for the least experienced staff.

Read those numbers as a founder and it is an innovation. Read them as an economist and it is a warning. When the cost of producing something falls, more of it gets produced. The result is not only faster startups. It is a crowded market in which customers must sort through a growing supply of polished, plausible and increasingly similar offers.

The first impressive demo is now close to the cheapest part of building a company. What has not become cheaper is the customer’s cost of believing you.

The supply shock

Generative AI behaves like a positive supply shock to digital production. It lowers the time and labour needed to create a first version, which pushes the supply curve outward.

Two numbers show the scale. Running a system at roughly GPT-3.5 level became more than 280 times cheaper in under two years, according to Stanford’s AI Index. And the share of new founders building AI startups nearly tripled over the same stretch, from around one in seven of the Stripe Atlas cohort at the start of 2023 to more than four in ten by the end of 2025. The input got cheap, and the queue got long.

Nor is adoption the thing separating anyone. Stanford’s 2026 index put organisational AI use at 88 per cent of surveyed organisations, with 70 per cent using generative AI in at least one business function. Whatever these tools confer, your competitors and your customers already have it.

Neither experiment proves that AI can build a durable company. One measured a bounded programming task; the other examined support work inside a single organisation. What they do show is that AI can reduce the cost of important production inputs and spread useful know-how. That matters because lower input costs reduce barriers to entry. More founders can test an idea, smaller teams can ship, and competitors can imitate visible features faster.

The same force that benefits one startup also benefits its rivals. If every team can generate a capable chatbot, dashboard or campaign, possessing one creates less economic rent. Speed still matters, but its advantage decays quickly when the tool that created the feature is broadly available. The strategic question therefore changes from “Can we build it?” to “Why will a customer choose us, believe us and stay?”

Where the scarcity went

Customers cannot fully evaluate a young company’s product quality before buying. Economists call this information asymmetry: the seller knows more about the offer than the buyer.

George Akerlof’s “market for lemons” modelled what that does to a market, using second-hand cars. If buyers cannot tell a good car from a bad one, they will only pay what an average car is worth. Sellers of good cars withdraw at that price, the average falls, and the market decays until mostly bad cars remain. Uncertainty about quality punishes honest sellers alongside dishonest ones, precisely because honesty is invisible from outside.

AI intensifies this problem because low-cost tools can make an unproven company look mature. A polished website, confident pitch and long feature list are now weaker signals than they once were.

Buyers respond by demanding stronger evidence: references, measurable outcomes, security reviews, integrations and credible commitments. These are transaction costs, the costs of finding, evaluating, negotiating and safely adopting an offer, and AI may reduce the startup’s cost of writing software while leaving the customer’s cost of switching systems, training employees or risking a failed implementation largely intact.

A product can be inexpensive to make and still be expensive to trust.

η=(Pₛ × I) + (R × A)eᵁ + N

Innovation (I) meeting uncertainty (U): a numerator driver against a denominator friction. When a driver rises for every competitor at once, it stops differentiating, and the ratio is decided in the denominator.

The Entrepreneurial Efficiency Equation (η) · Dr. Hafiz Muhammad Ali

Problem-solving velocity is a numerator driver, and AI raised it for everyone at the same moment. But velocity multiplies with innovation rather than adding to it, as the efficiency equation sets out, and a capability every competitor now holds cannot differentiate anyone. The AI tools lifted one half of the discovery pair and flattened the other. Uncertainty, in the denominator, did not move at all: the buyer’s doubt about whether you specifically will deliver sits exactly where it sat before the tools arrived. The numerator got louder and the denominator got decisive.

If honesty is invisible, it has to be signalled, and Michael Spence showed that a signal only works when it is costly to fake. The expense is the entire mechanism. Anything cheap to produce tells a buyer nothing, because everyone can produce it.

Which tells you what to build. Not another feature, but the assets whose value rises exactly as production gets cheaper.

Who ends up with the money, when an innovation is easy to copy, is usually not the inventor. David Teece traced the returns to whoever controls the capabilities needed to bring it to market: distribution, brand, specialised market knowledge, implementation, support. His own example was the CT scanner, which EMI invented and General Electric went on to sell. The code creates value. The surrounding system decides who keeps it.

What speed still buys

The strongest case against all this is that speed does not flatten. It compounds.

A team shipping twice as fast reaches its tenth customer conversation while a rival is still on its fourth. The tools are common; the learning they produce is not. A year of that gap is a real advantage, and no competitor can generate it on demand. This is not a weak objection. It is the one the numbers at the top of this article support.

But look at where the compounding actually lives. It is in the customer contact, not in the code. Two founders with identical assistants and identical repositories diverge only if one of them spends the saved time in front of buyers, hearing which one said no and why, which objection ended the deal, what broke during the pilot. Speed used to build produces more product. Speed used to test the product against real buyers produces knowledge, and a competitor cannot generate that knowledge, because acquiring it means having lost the deals that taught it.

So the objection survives, and it changes nothing about the demo. Speed compounds into learning, and learning is one of the assets a buyer will pay for. The advantage has still left the product.

The premise has a boundary worth naming. In biotechnology, advanced hardware, defence and regulated finance, generation was never the binding step. Patents, scientific expertise, physical plant and regulatory clearance still hold, because what is scarce in those markets is feasibility rather than belief. A founder there is answering whether the thing works at all, which is a different constraint and a different piece of writing. Everything here assumes the opposite condition: that your core feature is visible, digital, and reproducible with tools your competitor already has.

Under that condition the lead is a budget, not a moat. You have some months before the feature is ordinary. The decision is what you buy with them, and buying more capacity before you know which asset is binding is its own failure mode.

What cannot be generated

If the buyer’s problem is that he cannot tell a good company from a plausible one, then what you need is not a better product. It is something that proves the difference.

Four assets do that, and they share one property. Each is expensive to acquire, which is why a generative model cannot hand your competitor an equivalent next week.

1. A narrow beachhead.

   Specificity is an economic advantage, not a marketing preference. Serving one sharply defined group lowers the buyer’s search cost and your acquisition waste at the same time. “AI for healthcare” is a category. “Reducing denied claims for independent physical therapy clinics” is a job with an owner, a budget and an observable outcome. The sentence is cheap to write and the position is expensive to occupy: a competitor can copy the wording this afternoon and still not know which denial codes recur, which clearinghouse fails, or what the office manager does at month end. Narrow positioning also compounds learning, because the feedback concerns comparable problems rather than unrelated ones.

2. Proof that travels.

   Convert early delivery into evidence that survives leaving the room: a paid pilot, a verified result, a referenceable customer, a before-and-after metric, and an honest account of what did not work. A generic testimonial says a customer was pleased, which costs an email to obtain, and buyers price it accordingly. A documented reduction in processing time states the baseline, the intervention and the measured outcome, and nobody can write one without having done the work. Build it as a reusable asset rather than letting it disappear inside a private project.

3. A low-risk path to adoption.

   Founders optimise capability. Buyers optimise downside. The winning offer is often the one with the simplest migration, the fastest onboarding, the clearest data policy and the most credible exit. This is the transaction cost from earlier, met directly. None of it is glamorous, and all of it is engineering and operational work rather than words on a page, which is why a rival cannot claim it without building it. In an imitative market, reliability is a product feature.

4. Distribution that compounds.

   Paid reach stops when spending stops. Compounding distribution improves with use: referrals, partner channels, proprietary benchmarks, communities whose members create value for one another. A large follower count is not a network effect. The test is whether an additional participant makes the system more valuable to the participants already there. This is the hardest of the four to imitate, because it cannot be bought at any price short of years.

You will not build all four this year. Start with the one that answers the doubt your buyer already has, which you can only identify by having lost a deal and understood why.

A diagnostic

Which asset to build first is not a matter of preference. It is a matter of which doubt is currently costing you deals.

WORKING TOOL

What about your company is expensive to acquire?

1. If a competent rival pointed the same tools at your product tomorrow, what would they still be missing in ninety days?

   Name it concretely, and reject any answer that is a head start. Months are not a position. If the answer is a set of customer results they would have to earn one deal at a time, you have something worth defending.

2. What did your strongest piece of evidence cost to produce?

   Work out what a rival would have to spend to obtain the equivalent. If the answer is an afternoon, buyers are pricing it at an afternoon, whatever it says. Cheap evidence persuades nobody, because the buyer knows what it cost you.

3. What does the buyer risk by being wrong about you?

   Price the failure from their side: the migration, the retraining, the internal standing of whoever signed. That number, not your feature list, is what your evidence has to outweigh before anyone can say yes.

4. Does each customer make the next one cheaper to win?

   Compare what your last ten cost to acquire. If the line is flat, you have output rather than compounding, and no amount of production speed will bend it. Impressions, sign-ups and generated volume are not traction.

The first two questions test what you have. The second two test what it is worth to the person deciding. A founder who can answer all four knows which of the four assets is binding, and the answer is rarely the one he most enjoys building.

Conclusion

Generative AI has not made entrepreneurship easier. It has moved where the difficulty lives.

It used to live in production. Building the thing was the hard part, so a working product was proof that you could do hard things. Production is now faster and more widely available, which is good for innovation and better for customers. It also means the product proves only access.

It has moved to the buyer. Velocity rose for you and for every competitor at once, so the gain cancels. Uncertainty never moved. His doubt about whether you specifically will deliver sits exactly where it sat before the tools arrived, and it is now the term deciding the outcome.

So the lead the tools bought you is a budget, not a moat, and it is short. Spend it where it lowers the buyer’s risk rather than your own cost.

The founders who last will not be the ones who built more cheaply. They will be the ones who spent that lead on what stays expensive: a position narrow enough to know something, evidence nobody can write without having earned it, and a path to adoption that costs the buyer little.